Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network

被引:0
|
作者
Shi, Yeping [1 ,2 ]
Shi, Yunbo [1 ,3 ,4 ]
Niu, Haodong [1 ,3 ,4 ]
Liu, Jinzhou [1 ,3 ,4 ]
Sun, Pengjiao [2 ]
机构
[1] Harbin Univ Sci & Technol, Higher Educ Key Lab Measuring & Control Technol &, Harbin, Peoples R China
[2] Jilin Technol Coll Elect Informat, Elect & Commun Engn Sch, Jilin, Jilin, Peoples R China
[3] Harbin Univ Sci & Technol, Heilongjiang Prov Key Lab Laser Spect Technol & A, Harbin, Peoples R China
[4] Harbin Univ Sci & Technol, Natl Expt Teaching Demonstrat Ctr Measurement & C, Harbin, Peoples R China
来源
PLOS ONE | 2024年 / 19卷 / 12期
基金
中国国家自然科学基金;
关键词
DYNAMICS; OIL;
D O I
10.1371/journal.pone.0309228
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. We design and develop an electronic nose system containing a bionic chamber that imitates the nasal-cavity structure of humans and canines. The sensors are positioned based on fluid simulation results. Response data for ammonia and ethanol gases and the response/ recovery times of an ammonia sensor under three concentrations are collected using the electronic nose system. Response data are classified and regressed using a sparrow search algorithm (SSA)-optimized backpropagation neural network (BPNN). The results show that the sensor has a relative mean deviation of 1.45%. The ammonia sensor's output voltage is 1.3-2.05 V when the ammonia concentration ranges from 15 to 300 ppm. The ethanol gas sensor's output voltage is 1.89-3.15 V when the ethanol gas concentration ranges from 8 to 200 ppm. The average response time of the ammonia sensor in the chamber is 13 s slower than that of the sensor directly exposed to the gas being measured, while the average recovery time is 19 s faster. In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. It also outperforms the other models at regression prediction, with smaller absolute, mean absolute, and root mean square errors. Its coefficient of determination (R-2) is greater than 0.99, surpassing those of the SVM and RF models. The theoretical and experimental results both indicate that the proposed electronic nose system containing a bionic chamber, when used with the SSA-BPNN, offers a promising approach for detecting ammonia in livestock- and poultry-breeding facilities.
引用
收藏
页数:21
相关论文
共 49 条
  • [1] Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach
    Yan Y.
    Sun T.
    Ren T.
    Ding L.
    Mathematical Biosciences and Engineering, 2024, 21 (03) : 3519 - 3539
  • [2] Research on the safety early warning of Dangerous Chemicals based on Sparrow Search Algorithm and Genetic Algorithm-Optimized BP Neural Network
    Li, Pan
    Guo, Jian
    Zhu, Baikang
    Hong, Bingyuan
    PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ALGORITHMS, SOFTWARE ENGINEERING, AND NETWORK SECURITY, ASENS 2024, 2024, : 696 - 699
  • [3] Enhancing Damage Detection Using Reptile Search Algorithm-Optimized Neural Network and Frequency Response Function
    Khatir, A.
    Capozucca, R.
    Khatir, S.
    Magagnini, E.
    Cuong-Le, Thanh
    JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES, 2025, 13 (01)
  • [4] A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique
    Deng, Wu
    He, Qi
    Zhou, Xiangbing
    Chen, Huayue
    Zhao, Huimin
    PHYSICA SCRIPTA, 2023, 98 (11)
  • [5] Lithium battery SOC estimation based on improved sparrow search algorithm and backpropagation neural network
    Zhang, Yingying
    Wang, Ruilin
    Shen, Yueteng
    Zhao, Yu
    Chen, Zhiwei
    AIP ADVANCES, 2024, 14 (11)
  • [6] EOR screening using optimized artificial neural network by sparrow search algorithm
    Tabatabaei, S. Mostafa
    Attari, Nikta
    Panahi, S. Amirali
    Asadian-Pakfar, Mojtaba
    Sedaee, Behnam
    GEOENERGY SCIENCE AND ENGINEERING, 2023, 229
  • [7] MODELING OF THIN FILM PROCESS DATA USING A GENETIC ALGORITHM-OPTIMIZED INITIAL WEIGHT OF BACKPROPAGATION NEURAL NETWORK
    Kim, Byungwhan
    Lee, Hwajune
    Kim, Donghwan
    APPLIED ARTIFICIAL INTELLIGENCE, 2009, 23 (02) : 168 - 178
  • [8] A genetic algorithm-optimized backpropagation neural network model for predicting soil moisture content using spectral data
    Wang, Jiawei
    Wu, Yongyi
    Zhang, Yulu
    Wang, Honghao
    Yan, Hong
    Jin, Hua
    JOURNAL OF SOILS AND SEDIMENTS, 2024, 24 (07) : 2816 - 2828
  • [9] Multistrategy Improved Sparrow Search Algorithm Optimized Deep Neural Network for Esophageal Cancer
    Wang, Yanfeng
    Liu, Qing
    Sun, Junwei
    Wang, Lidong
    Song, Xin
    Zhao, Xueke
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [10] Sparrow search algorithm-optimized variational mode decomposition-based multiscale convolutional network for cavitation diagnosis of hydro turbines
    Li, Feng
    Wang, Chaoge
    Liu, Zhiliang
    Huang, Yuanyuan
    Wang, Tianzhen
    OCEAN ENGINEERING, 2024, 312